Legal claims defining the scope of protection. Each claim is shown in both the original legal language and a plain English translation.
2. The computer implemented method of claim 1 further comprising: creating the historical data lookup table based on the received first set of inputs and the second set of inputs and the estimated effective pest severity index; appending the historical data lookup table with the actual effective pest severity index; and updating the pest forecasting model and the natural enemies forecasting model based on the actual effective pest severity index.
This invention relates to agricultural pest management systems that use predictive modeling to forecast pest severity and natural enemy populations. The system addresses the challenge of accurately predicting pest outbreaks and the effectiveness of natural predators in controlling them, which is critical for sustainable farming practices. The method involves receiving a first set of inputs related to environmental conditions, such as temperature, humidity, and crop type, and a second set of inputs related to pest and natural enemy populations, such as species identification and population counts. A pest forecasting model and a natural enemies forecasting model are then trained using these inputs to estimate an effective pest severity index, which quantifies the impact of pests on crops while accounting for the presence of natural enemies. The system creates a historical data lookup table that stores the received inputs and the estimated effective pest severity index. After actual pest severity data is collected, the lookup table is updated with the actual effective pest severity index. The pest forecasting model and the natural enemies forecasting model are then refined based on this actual data to improve future predictions. This iterative process ensures that the models adapt over time, enhancing their accuracy and reliability for pest management decisions.
3. The computer implemented method of claim 2 , wherein updating the pest forecasting model and the natural enemies forecasting model is further based on management practices deployed in the geo-location under consideration.
This invention relates to agricultural pest management systems that use predictive modeling to forecast pest populations and natural enemy populations in specific geographic locations. The system addresses the challenge of optimizing pest control by integrating real-time data and adaptive modeling to improve decision-making for farmers and agricultural managers. The method involves collecting environmental and agricultural data from sensors or other sources in a target geographic area. This data is used to train and update two distinct forecasting models: a pest forecasting model that predicts pest population dynamics and a natural enemies forecasting model that predicts the population of beneficial organisms that control pests. The models are continuously refined using machine learning techniques to enhance accuracy over time. A key aspect of the invention is the incorporation of management practices—such as pesticide applications, crop rotation, or biological control measures—into the model updates. By analyzing how these practices influence pest and natural enemy populations, the system provides more precise forecasts and recommendations for sustainable pest management. The models can then generate actionable insights, such as optimal timing for interventions or adjustments to farming practices, to minimize pest damage while preserving beneficial species. This adaptive approach reduces reliance on chemical controls and promotes environmentally friendly agricultural practices.
4. The computer implemented method of claim 1 , further comprising optimizing pesticide application based on the estimated effective pest severity index.
This invention relates to precision agriculture, specifically optimizing pesticide application in farming to reduce environmental impact and costs. The method addresses the challenge of inefficient pesticide use, which can lead to over-application, environmental harm, and increased expenses. The system estimates a pest severity index by analyzing data from sensors, weather forecasts, and historical pest patterns. This index quantifies the likelihood and intensity of pest infestations across a field. The method then adjusts pesticide application rates dynamically, applying higher concentrations in high-risk areas and reducing or eliminating application in low-risk zones. This targeted approach minimizes pesticide waste while ensuring effective pest control. The optimization process may also consider factors like crop type, growth stage, and soil conditions to further refine application strategies. By integrating real-time data and predictive analytics, the system enables farmers to apply pesticides more precisely, improving sustainability and economic efficiency. The invention aims to balance pest management with environmental and financial considerations, making agriculture more precise and responsible.
6. The system of claim 5 , wherein the one or more processors are further configured to: create the historical data lookup table based on the received first set of inputs and the second set of inputs and the estimated effective pest severity index; append the historical data lookup table with the actual effective pest severity index; and update the pest forecasting model and the natural enemies forecasting model based on the actual effective pest severity index.
The system relates to agricultural pest management, specifically improving forecasting models for pest severity and natural enemy populations. The problem addressed is the need for accurate, data-driven predictions to optimize pest control strategies while minimizing environmental impact. The system uses a historical data lookup table to refine forecasting models by comparing estimated and actual pest severity indices. The system receives a first set of inputs, such as environmental conditions, crop data, and pest observations, and a second set of inputs, such as natural enemy populations and control measures. It generates an estimated effective pest severity index, which is then used to create a historical data lookup table. This table is updated with the actual effective pest severity index once it becomes available, allowing the system to compare predictions with real-world outcomes. The pest forecasting model and the natural enemies forecasting model are then updated based on this comparison, improving future predictions. The system dynamically adjusts its models to enhance accuracy over time, supporting more effective pest management decisions.
7. The system of claim 6 , wherein the one or more processors are further configured to update the pest forecasting model and the natural enemies forecasting model based on management practices deployed in the geo-location under consideration.
The system relates to agricultural pest management, specifically forecasting pest populations and their natural enemies to optimize control strategies. The problem addressed is the need for dynamic, location-specific pest management that adapts to real-world conditions and interventions. Traditional methods often rely on static models or broad regional data, leading to ineffective or overuse of pesticides. The system includes a pest forecasting model and a natural enemies forecasting model, both trained on historical and real-time data from a specific geographic location. These models predict pest population trends and the presence of beneficial organisms that naturally suppress pests. The system further updates these models based on management practices applied in the area, such as pesticide use, crop rotation, or biological control introductions. By incorporating these interventions, the models refine their accuracy over time, ensuring forecasts align with current conditions. The system may also generate recommendations for pest control actions, balancing effectiveness with environmental impact. This adaptive approach reduces reliance on chemical pesticides and promotes sustainable agriculture.
8. The system of claim 5 , wherein the one or more processors are further configured to optimize pesticide application based on the estimated effective pest severity index.
A system for optimizing pesticide application in agricultural environments addresses the challenge of inefficient pesticide use, which can lead to environmental harm, increased costs, and reduced crop yields. The system uses data from sensors, weather forecasts, and historical pest data to estimate a pest severity index, which quantifies the likelihood and intensity of pest infestations. This index is dynamically adjusted based on real-time environmental conditions, such as temperature, humidity, and wind speed, to improve accuracy. The system then optimizes pesticide application by determining the precise timing, dosage, and target areas for treatment, ensuring that pesticides are applied only when and where they are most needed. This reduces unnecessary chemical use, minimizes environmental impact, and enhances crop protection. The system may also integrate with automated spraying equipment to execute the optimized application strategy, further improving efficiency. By leveraging predictive analytics and real-time data, the system provides a more sustainable and cost-effective approach to pest management in agriculture.
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February 11, 2020
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